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Cryptosporidium as a testbed for single cell genome characterization of unicellular eukaryotes

Overview of attention for article published in BMC Genomics, June 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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23 X users
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1 Facebook page
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1 Google+ user

Citations

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42 Dimensions

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77 Mendeley
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Title
Cryptosporidium as a testbed for single cell genome characterization of unicellular eukaryotes
Published in
BMC Genomics, June 2016
DOI 10.1186/s12864-016-2815-y
Pubmed ID
Authors

Karin Troell, Björn Hallström, Anna-Maria Divne, Cecilia Alsmark, Romanico Arrighi, Mikael Huss, Jessica Beser, Stefan Bertilsson

Abstract

Infectious disease involving multiple genetically distinct populations of pathogens is frequently concurrent, but difficult to detect or describe with current routine methodology. Cryptosporidium sp. is a widespread gastrointestinal protozoan of global significance in both animals and humans. It cannot be easily maintained in culture and infections of multiple strains have been reported. To explore the potential use of single cell genomics methodology for revealing genome-level variation in clinical samples from Cryptosporidium-infected hosts, we sorted individual oocysts for subsequent genome amplification and full-genome sequencing. Cells were identified with fluorescent antibodies with an 80 % success rate for the entire single cell genomics workflow, demonstrating that the methodology can be applied directly to purified fecal samples. Ten amplified genomes from sorted single cells were selected for genome sequencing and compared both to the original population and a reference genome in order to evaluate the accuracy and performance of the method. Single cell genome coverage was on average 81 % even with a moderate sequencing effort and by combining the 10 single cell genomes, the full genome was accounted for. By a comparison to the original sample, biological variation could be distinguished and separated from noise introduced in the amplification. As a proof of principle, we have demonstrated the power of applying single cell genomics to dissect infectious disease caused by closely related parasite species or subtypes. The workflow can easily be expanded and adapted to target other protozoans, and potential applications include mapping genome-encoded traits, virulence, pathogenicity, host specificity and resistance at the level of cells as truly meaningful biological units.

X Demographics

X Demographics

The data shown below were collected from the profiles of 23 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 3%
Russia 1 1%
Norway 1 1%
Unknown 73 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 25%
Researcher 10 13%
Student > Bachelor 6 8%
Student > Master 6 8%
Professor > Associate Professor 5 6%
Other 14 18%
Unknown 17 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 23%
Agricultural and Biological Sciences 17 22%
Immunology and Microbiology 7 9%
Environmental Science 4 5%
Engineering 4 5%
Other 6 8%
Unknown 21 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 September 2016.
All research outputs
#2,216,331
of 24,164,942 outputs
Outputs from BMC Genomics
#592
of 10,914 outputs
Outputs of similar age
#40,271
of 359,033 outputs
Outputs of similar age from BMC Genomics
#14
of 174 outputs
Altmetric has tracked 24,164,942 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,914 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 359,033 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 174 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.